BANKA SEÇİM TERCİHLERİNİN BULANIK KÜMELERE DAYALI YENİ BİR KARAR VERME ÇERÇEVESİ İLE DEĞERLENDİRİLMESİ

Year 2020, Volume: 19 Issue: Temmuz 2020(Özel Ek) - Prof. Dr. Sabri ORMAN Özel Sayısı, 73 - 94, 31.07.2020

Merve Koçak , Ahmet Çalık

Abstract

Günümüzde, artan sayıda banka kuruluşu, müşteri ilişkilerini geliştirmek ve pazar paylarını artırmak için çeşitli araçlar uygulayarak müşteri odaklı olmaya çalışmaktadırlar. Bu çalışmada, müşterilerin banka seçim sürecindeki tercih nedenlerini araştırmak ve belirlenen bankalar arasından sıralama yapmak için bir Çok Kriterli Karar Verme (ÇKKV) çerçevesi önerilmektedir. Analitik hiyerarşi prosesi (AHP) hem klasik mantık hem de bulanık mantık ortamları altında uygulanarak karar vericilerin görüşlerindeki belirsizlik daha iyi yansıtılmış ve TOPSIS yöntemi ile alternatif bankaların sıralaması araştırılmıştır. Modelimizde altı kriter (mevduat faiz oranı, kredi faiz oranı, ATM sayısı, ücret ve komisyonlar, tavsiye ve personel özellikleri) ve beş banka temelinde geliştirilmiştir. Bir dış ticaret şirketinin üç uzmanı ile bir görüşme gerçekleştirilmiş; üç uzmandan alınan yanıtlar, en önemli kriterlerin kredi faiz oranı, mevduat faiz oranı ve ücretler ve komisyonlar, tavsiyenin ise en önemsiz kriter olduğunu göstermektedir. Ayrıca AHP ve TOPSIS yöntemleri kullanılarak dördüncü banka en uygun alternatif olarak seçilmiştir. Bu çalışma, AHP ve TOPSIS yöntemleri yardımıyla banka seçim süreci için farklı bulanık ortamlarda bir karar verme çerçevesi önererek bu konuyu ele almak isteyen uzmanların karar vermelerine yardımcı olmaktadır.

Keywords

AHP, Aralık Tip-2 Bulanık AHP, Banka Seçimi, Tip-1 Bulanık AHP, TOPSIS

EVALUATION OF BANK SELECTION PREFERENCES WITH A NOVEL DECISION-MAKING FRAMEWORK BASED ON FUZZY SETS

Abstract

Today, an increasing number of bank institutions are trying to be customer-focused by applying various tools to improve customer relations and increase their market shares. In this study, a Multi-Criteria Decision Making (MCDM) framework is proposed to investigate the reasons for customers' preference in the bank selection process and to rank among the determined banks. The Analytic Hierarchy Process (AHP) is applied under both classical logic and fuzzy logic environments, and the uncertainty in the opinions of the decision-makers is better reflected and the ranking of alternative banks is investigated with the TOPSIS method. Our model has been developed on the basis of six criteria (interest rate deposits, interest rate on loans, number of ATMs, fees and commissions, recommendation and staff characteristics) and five banks. An interview is held with three experts of a foreign trade company; The answers from three experts show that the most important criteria are the interest rate on loans and interest rate deposits, the least important criteria is recommendation. The fourth bank is chosen as the most suitable alternative by using AHP and TOPSIS methods. The proposed framework for bank selection helps experts who want to address the issue.

Keywords

AHP, Interval Type-2 Fuzzy AHP, Bank Selection, Type-1 Fuzzy AHP, TOPSIS

References

• Akkoç, S., & Vatansever, K. (2013). Fuzzy Performance Evaluation with AHP and Topsis Methods: Evidence from Turkish Banking Sector after the Global Financial Crisis. Eurasian Journal of Business and Economics, 6(11), 53–74.
• Al-Shammari, M., & Mili, M. (2019). A fuzzy analytic hierarchy process model for customers’ bank selection decision in the Kingdom of Bahrain. Operational Research. https://doi.org/10.1007/s12351-019-00496-y
• Alferos, D. B., & Cristobal, E. J. S. (2017). Bank Selection Criteria by the Students : Input to the Banking Sector of the Philippines. 5(2), 5–15.
• Aliouat, B., Moez, L., Hikkerova, L., & Gharbi, J. (2016). The determinants of the choice of Islamic banks in Tunisia. International Journal of Bank Marketing, 34(5), 710–730. https://doi.org/10.1108/IJBM-11-2014-0170
• Almossawi, M. (2001). Bank selection criteria employed by college students in Bahrain: an empirical analysis. International Journal of Bank Marketing, 19(3), 115–125. https://doi.org/10.1108/02652320110388540
• Altınırmak, S., Ergün, M., Karamaşa, Ç., Şen, O., Aytekin, A., & Okoth, B. O. (2019). Birikim Değerlendirme Tercihi İle Banka Seçimi Arasındaki İlişkinin Multinominal Lojistik Regresyon İle Analiz Edilmesi: Eskişehir Örneği. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22(41), 197–218. https://doi.org/10.31795/baunsobed.581342
• Bayer, S., Gimpel, H., & Sarikaya, S. (2019). Bank customers’ decision-making process in choosing between ethical and conventional banking: a survey-based examination. Journal of Business Economics, 89(6), 655–697. https://doi.org/10.1007/s11573-019-00934-5
• Blankson, C., Mbah, C. H. N., & Owusu-Frempong, L. Y. (2009). The Development of a Scale Measuring Consumers’ Selection of Retail Banks in Ghana. Journal of African Business, 10(2), 182–202. https://doi.org/10.1080/15228910903187742
• Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
• Cebeci, İ., & Çabuk, Z. (2016). Tüketicilerin Banka Tercihini Etkileyen Faktörlerin Belirlenmesi: Giresun’da Bir Araştırma. Finansal Araştırmalar ve Çalışmalar Dergisi, 8(14), 57. https://doi.org/10.14784/jfrs.35440
• Chigamba, C., & Fatoki, O. (2011). Factors Influencing the Choice of Commercial Banks by University Students in South Africa. International Journal of Business and Management, 6(6), 1833–8119. https://doi.org/10.5539/ijbm.v6n6p66
• Cicic, M., Brkic, N., & Agic, E. (2004). Bank selection criteria employed by students in a southeastern European country: an empirical analysis of potential market segments’ preferences. International Journal of Bank Marketing, 27(2), 1–18.
• Erol, C., Baklaci, H. F., Aydoğan, B., & Gökçe, T. (2014). Performance comparison of Islamic (participation) banks and commercial banks in Turkish banking sector. EuroMed Journal of Business, 9(2), 114–128.
• García, F., Guijarro, F., & Moya, I. (2010). Ranking Spanish savings banks: A multicriteria approach. Mathematical and Computer Modelling, 52(7–8), 1058–1065. https://doi.org/10.1016/J.MCM.2010.02.015
• Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications. Springer-Verlag. https://books.google.com.tr/books?id=X-wYAQAAIAAJ
• Javalgi, R. G., Armacost, R. L., & Hosseini, J. C. (1989). Using the analytic hierarchy process for bank management: Analysis of consumer bank selection decisions. Journal of Business Research, 19(1), 33–49. https://doi.org/10.1016/0148-2963(89)90039-8
• Joshi, D., & Kumar, S. (2014). Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making. Egyptian Informatics Journal, 15(2), 97–104. https://doi.org/10.1016/J.EIJ.2014.03.002
• Kahraman, C., Öztayşi, B., Uçal Sarı, İ., & Turanoğlu, E. (2014). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48–57. https://doi.org/10.1016/J.KNOSYS.2014.02.001
• Karadeniz, M., & Gözüyukarı, M. (2016). Bankacılık Sektöründe Algılanan Hizmet Kalitesinin Müşteri Memnuniyeti Üzerine Etkisi. Yönetim Bilimleri Dergisi, 14(28), 533–552.
• Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30–37. https://doi.org/10.1016/j.econmod.2014.07.036
• Nkamnebe, A. D., Ukenna, S., Anionwu, C., & Chibuike, V. (2014). Determinants of bank selection by university undergrads in south east nigeria: Empirical evidence. African Journal of Economic and Management Studies, 5(3), 369–382. https://doi.org/10.1108/AJEMS-08-2011-0054
• Paltayian, G. N., Georgiou, A. C., Gotzamani, K. D., & Andronikidis, A. I. (2012). An integrated framework to improve quality and competitive positioning within the financial services context. International Journal of Bank Marketing, 30(7), 527–547. https://doi.org/10.1108/02652321211274282
• Phuong Ta, H., & Yin Har, K. (2000). A study of bank selection decisions in Singapore using the Analytical Hierarchy Process. International Journal of Bank Marketing, 18(4), 170–180. https://doi.org/10.1108/02652320010349058
• Rehman, H., & Ahmad, S. (2008). An empirical analysis of the determinants of bank selection in Pakistan: A Customer View. Pakistan Economic and Social Review, 46(2), 147–160.
• Ren, P., Xu, Z., & Gou, X. (2016). Pythagorean fuzzy TODIM approach to multi-criteria decision making. Applied Soft Computing, 42, 246–259. https://doi.org/10.1016/J.ASOC.2015.12.020
• Sayani, H., & Miniaoui, H. (2013). Determinants of bank selection in the United Arab Emirates. International Journal of Bank Marketing, 31(3), 206–228. https://doi.org/10.1108/02652321311315302
• Seçme, N. Y., Bayrakdaroğlu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36(9), 11699–11709. https://doi.org/10.1016/J.ESWA.2009.03.013
• Srouji, A. F., Halim, M. S. A., Lubis, Z., & Hamdallah, M. E. (2015). Determinants of Bank Selection Criteria’S in Relation To Jordanian Islamic and Conventional Banks. International Journal of Economics, Commerce and Management United Kingdom, 3(10), 294–306.
• Stankevičienė, J., & Mencaitė, E. (2012). The evaluation of bank performance using a multicriteria decision making model: a case study on Lithuanian commercial banks. Technological and Economic Development of Economy, 18(1), 189–205. https://doi.org/10.3846/20294913.2012.668373
• Tavana, M., Kiani Mavi, R., Santos-Arteaga, F. J., & Rasti Doust, E. (2016). An extended VIKOR method using stochastic data and subjective judgments. Computers & Industrial Engineering, 97, 240–247. https://doi.org/10.1016/J.CIE.2016.05.013